Nytt fordon för transport av bränsleflis : Slutrapport (STEM-projekt 22127-1)

Högskolan Dalarna har i samarbete med Naturbränsle i Mellansverige AB genomfört studier på ett nytt fordon för transport av skogsflis. Fordonet är försett med egen lastningsutrustning (kran och skopa), vilket innebär att flisskördaren kan tippa flisen direkt på marken eller på en i förväg utlagd duk (minskar risken för föroreningar i samband med lastning). Studier har också genomförts på transport av flis med lastväxlarfordon och container (traditionell metod) för att jämförelser skall kunna göras mellan de olika fordonstyperna. Studierna har finansie¬rats via anslag från Statens Energimyndighet och via ”naturabidrag” från deltagande företag.Studierna visar att det nya transportfordonet är ett bättre alternativ än de traditionella last¬växlarfordonen på större objekt med långa transportavstånd. Dessutom pekar studien på att det är sannolikt är bättre även på små objekt under förutsättning att flisningskostnaden kan hållas på en rimlig nivå. Studierna visar också att det bör finnas en viss utvecklingspotential på det nya fordonet (teknik- och metodutveckling), varför det bedöms kunna konkurrera med lastväxlarfordonen även på andra typer av objekt.Jämfört med de traditionella lastväxlarfordonen har det nya fordonet bl.a. följande fördelar:•Transportarbetet blir lättare att planera i och med att beroendeförhållandet mellan in¬blandade maskiner och fordon för flisproduktion och transport upphör.••Risken för störningar i transportflödet minskar.•Miljövinsterna blir större jämfört med lastväxlarfordon som måste ställa ut tomma con¬tainrar innan flisningen kan påbörjas.•Det finns inget behov av lastmaskiner på terminalerna.•Flis kan mellanlagras i skogen.Till nackdelarna med det nya fordonet hör bl.a. följande:•Framkomligheten är något sämre än för lastväxlarfordon på mycket smala och kurviga skogsbilvägar.•Det finns en viss risk för att föroreningar följer med vid lastning av fordonet. Studies were carried out on a new vehicle for transport of fuel chips from the forest. The vehicle was equipped with a crane and a bucket meaning that the chipper may tip the fuel chips right on the ground or on a mat (vira from wood processing industry) to prevent from dirt such as sand and stones when loading. Studies were also carried out on traditional main hauling with transport bins. Transport speed was the same for all vehicles except for the new self-loader on forest roads with lower quality.The studies show that the new system probably is a better alternative on large sites with long transport distances and on sites with only little parking place for transport bins. It is also likely that the new vehicle may be used on very small sites if they are close to each other and if moving cost for the chipper is low.The studies show that the new vehicle has the following advantages:•Transport and other work may be planned in a better way leading to that stress de¬creases.•Dependence between chipper operators and truck drivers decreases.•The risk for disturbances in transport flow decreases.•Environment benefits compared to traditional system with higher traffic intensity (less exhaust gases and lower stress on roads and bridges).•No need for loading machines on terminals.•Easier to store fuel chips on landing.

Speed prediction for triggering vehicle activated signs

Accurate speed prediction is a crucial step in the development of a dynamic vehcile activated sign (VAS). A previous study showed that the optimal trigger speed of such signs will need to be pre-determined according to the nature of the site and to the traffic conditions. The objective of this paper is to find an accurate predictive model based on historical traffic speed data to derive the optimal trigger speed for such signs. Adaptive neuro fuzzy (ANFIS), classification and regression tree (CART) and random forest (RF) were developed to predict one step ahead speed during all times of the day. The developed models were evaluated and compared to the results obtained from artificial neural network (ANN), multiple linear regression (MLR) and naïve prediction using traffic speed data collected at four sites located in Sweden. The data were aggregated into two periods, a short term period (5-min) and a long term period (1-hour). The results of this study showed that using RF is a promising method for predicting mean speed in the two proposed periods.. It is concluded that in terms of performance and computational complexity, a simplistic input features to the predicitive model gave a marked increase in the response time of the model whilse still delivering a low prediction error.

A Microdata Analysis Approach to Transport Infrastructure Maintenance

Maintenance of transport infrastructure assets is widely advocated as the key in minimizing current and future costs of the transportation network. While effective maintenance decisions are often a result of engineering skills and practical knowledge, efficient decisions must also account for the net result over an asset's life-cycle. One essential aspect in the long term perspective of transport infrastructure maintenance is to proactively estimate maintenance needs. In dealing with immediate maintenance actions, support tools that can prioritize potential maintenance candidates are important to obtain an efficient maintenance strategy. This dissertation consists of five individual research papers presenting a microdata analysis approach to transport infrastructure maintenance. Microdata analysis is a multidisciplinary field in which large quantities of data is collected, analyzed, and interpreted to improve decision-making. Increased access to transport infrastructure data enables a deeper understanding of causal effects and a possibility to make predictions of future outcomes. The microdata analysis approach covers the complete process from data collection to actual decisions and is therefore well suited for the task of improving efficiency in transport infrastructure maintenance. Statistical modeling was the selected analysis method in this dissertation and provided solutions to the different problems presented in each of the five papers. In Paper I, a time-to-event model was used to estimate remaining road pavement lifetimes in Sweden. In Paper II, an extension of the model in Paper I assessed the impact of latent variables on road lifetimes; displaying the sections in a road network that are weaker due to e.g. subsoil conditions or undetected heavy traffic. The study in Paper III incorporated a probabilistic parametric distribution as a representation of road lifetimes into an equation for the marginal cost of road wear. Differentiated road wear marginal costs for heavy and light vehicles are an important information basis for decisions regarding vehicle miles traveled (VMT) taxation policies. In Paper IV, a distribution based clustering method was used to distinguish between road segments that are deteriorating and road segments that have a stationary road condition. Within railway networks, temporary speed restrictions are often imposed because of maintenance and must be addressed in order to keep punctuality. The study in Paper V evaluated the empirical effect on running time of speed restrictions on a Norwegian railway line using a generalized linear mixed model.